Overview

Dataset statistics

Number of variables16
Number of observations320772
Missing cells1147694
Missing cells (%)22.4%
Duplicate rows5
Duplicate rows (%)< 0.1%
Total size in memory36.4 MiB
Average record size in memory119.1 B

Variable types

Text2
Categorical3
Numeric11

Alerts

Dataset has 5 (< 0.1%) duplicate rowsDuplicates
countries_fr has a high cardinality: 722 distinct valuesHigh cardinality
brands has a high cardinality: 58784 distinct valuesHigh cardinality
countries_fr is highly imbalanced (77.4%)Imbalance
product_name has 17762 (5.5%) missing valuesMissing
brands has 28412 (8.9%) missing valuesMissing
energy_100g has 59659 (18.6%) missing valuesMissing
salt_100g has 65262 (20.3%) missing valuesMissing
sodium_100g has 65309 (20.4%) missing valuesMissing
fiber_100g has 119886 (37.4%) missing valuesMissing
additives_n has 71833 (22.4%) missing valuesMissing
sugars_100g has 75801 (23.6%) missing valuesMissing
fat_100g has 76881 (24.0%) missing valuesMissing
saturated_fat_100g has 91218 (28.4%) missing valuesMissing
nutrition_score_uk_100g has 99562 (31.0%) missing valuesMissing
nutrition_score_fr_100g has 99562 (31.0%) missing valuesMissing
nutrition_grade_fr has 99562 (31.0%) missing valuesMissing
cholesterol_100g has 176682 (55.1%) missing valuesMissing
energy_100g is highly skewed (γ1 = 491.0039771)Skewed
salt_100g is highly skewed (γ1 = 493.5037928)Skewed
sodium_100g is highly skewed (γ1 = 493.458469)Skewed
fiber_100g is highly skewed (γ1 = 363.5478054)Skewed
cholesterol_100g is highly skewed (γ1 = 221.1178099)Skewed
energy_100g has 8909 (2.8%) zerosZeros
salt_100g has 34174 (10.7%) zerosZeros
sodium_100g has 34131 (10.6%) zerosZeros
fiber_100g has 68833 (21.5%) zerosZeros
additives_n has 94259 (29.4%) zerosZeros
sugars_100g has 37077 (11.6%) zerosZeros
fat_100g has 64504 (20.1%) zerosZeros
saturated_fat_100g has 68736 (21.4%) zerosZeros
nutrition_score_uk_100g has 13588 (4.2%) zerosZeros
nutrition_score_fr_100g has 12763 (4.0%) zerosZeros
cholesterol_100g has 89441 (27.9%) zerosZeros

Reproduction

Analysis started2024-06-07 15:44:09.372314
Analysis finished2024-06-07 15:44:35.447859
Duration26.08 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

code
Text

Distinct320749
Distinct (%)100.0%
Missing23
Missing (%)< 0.1%
Memory size2.4 MiB
2024-06-07T17:44:35.667111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length41
Median length13
Mean length12.763809
Min length1

Characters and Unicode

Total characters4093979
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique320749 ?
Unique (%)100.0%

Sample

1st row0000000003087
2nd row0000000004530
3rd row0000000004559
4th row0000000016087
5th row0000000016094
ValueCountFrequency (%)
0000000003087 1
 
< 0.1%
0000000016650 1
 
< 0.1%
0000000016094 1
 
< 0.1%
0000000016100 1
 
< 0.1%
0000000016117 1
 
< 0.1%
0000000016124 1
 
< 0.1%
0000000016193 1
 
< 0.1%
0000000018579 1
 
< 0.1%
0000000016513 1
 
< 0.1%
0000000016872 1
 
< 0.1%
Other values (320739) 320739
> 99.9%
2024-06-07T17:44:36.083999image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1023511
25.0%
1 457173
11.2%
3 391050
 
9.6%
2 388171
 
9.5%
7 330357
 
8.1%
4 329051
 
8.0%
5 315989
 
7.7%
8 304983
 
7.4%
6 301681
 
7.4%
9 252013
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4093979
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1023511
25.0%
1 457173
11.2%
3 391050
 
9.6%
2 388171
 
9.5%
7 330357
 
8.1%
4 329051
 
8.0%
5 315989
 
7.7%
8 304983
 
7.4%
6 301681
 
7.4%
9 252013
 
6.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4093979
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1023511
25.0%
1 457173
11.2%
3 391050
 
9.6%
2 388171
 
9.5%
7 330357
 
8.1%
4 329051
 
8.0%
5 315989
 
7.7%
8 304983
 
7.4%
6 301681
 
7.4%
9 252013
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4093979
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1023511
25.0%
1 457173
11.2%
3 391050
 
9.6%
2 388171
 
9.5%
7 330357
 
8.1%
4 329051
 
8.0%
5 315989
 
7.7%
8 304983
 
7.4%
6 301681
 
7.4%
9 252013
 
6.2%

countries_fr
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct722
Distinct (%)0.2%
Missing280
Missing (%)0.1%
Memory size648.4 KiB
États-Unis
172998 
France
94392 
Suisse
 
14953
Allemagne
 
7870
Espagne
 
5009
Other values (717)
25270 

Length

Max length211
Median length10
Mean length8.6033848
Min length4

Characters and Unicode

Total characters2757316
Distinct characters121
Distinct categories9 ?
Distinct scripts7 ?
Distinct blocks8 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique390 ?
Unique (%)0.1%

Sample

1st rowFrance
2nd rowÉtats-Unis
3rd rowÉtats-Unis
4th rowÉtats-Unis
5th rowÉtats-Unis

Common Values

ValueCountFrequency (%)
États-Unis 172998
53.9%
France 94392
29.4%
Suisse 14953
 
4.7%
Allemagne 7870
 
2.5%
Espagne 5009
 
1.6%
Royaume-Uni 4825
 
1.5%
Belgique 2595
 
0.8%
Australie 2056
 
0.6%
Russie 1315
 
0.4%
France,Suisse 1224
 
0.4%
Other values (712) 13255
 
4.1%

Length

2024-06-07T17:44:36.240605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
états-unis 172999
53.8%
france 94392
29.3%
suisse 14953
 
4.6%
allemagne 7870
 
2.4%
espagne 5009
 
1.6%
royaume-uni 4825
 
1.5%
belgique 2595
 
0.8%
australie 2056
 
0.6%
russie 1315
 
0.4%
france,suisse 1224
 
0.4%
Other values (747) 14549
 
4.5%

Most occurring characters

ValueCountFrequency (%)
s 393767
14.3%
t 352808
12.8%
a 305218
11.1%
n 297617
10.8%
i 209391
7.6%
- 180211
6.5%
U 179165
6.5%
É 173559
6.3%
e 162965
5.9%
r 105298
 
3.8%
Other values (111) 397317
14.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2061752
74.8%
Uppercase Letter 507351
 
18.4%
Dash Punctuation 180211
 
6.5%
Other Punctuation 6496
 
0.2%
Space Separator 1295
 
< 0.1%
Other Letter 205
 
< 0.1%
Decimal Number 3
 
< 0.1%
Nonspacing Mark 2
 
< 0.1%
Spacing Mark 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 23
 
11.2%
ل 20
 
9.8%
ة 13
 
6.3%
ع 12
 
5.9%
ي 10
 
4.9%
س 10
 
4.9%
ن 10
 
4.9%
د 8
 
3.9%
م 8
 
3.9%
7
 
3.4%
Other values (33) 84
41.0%
Lowercase Letter
ValueCountFrequency (%)
s 393767
19.1%
t 352808
17.1%
a 305218
14.8%
n 297617
14.4%
i 209391
10.2%
e 162965
7.9%
r 105298
 
5.1%
c 99674
 
4.8%
u 34763
 
1.7%
l 28158
 
1.4%
Other values (30) 72093
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
U 179165
35.3%
É 173559
34.2%
F 98516
19.4%
S 17524
 
3.5%
A 11625
 
2.3%
R 7798
 
1.5%
E 5403
 
1.1%
B 4608
 
0.9%
P 1870
 
0.4%
I 1813
 
0.4%
Other values (18) 5470
 
1.1%
Other Punctuation
ValueCountFrequency (%)
, 6274
96.6%
: 194
 
3.0%
' 28
 
0.4%
Decimal Number
ValueCountFrequency (%)
7 2
66.7%
6 1
33.3%
Nonspacing Mark
ValueCountFrequency (%)
1
50.0%
1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 180211
100.0%
Space Separator
ValueCountFrequency (%)
1295
100.0%
Spacing Mark
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2569094
93.2%
Common 188005
 
6.8%
Arabic 148
 
< 0.1%
Thai 38
 
< 0.1%
Han 18
 
< 0.1%
Cyrillic 9
 
< 0.1%
Devanagari 4
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 393767
15.3%
t 352808
13.7%
a 305218
11.9%
n 297617
11.6%
i 209391
8.2%
U 179165
7.0%
É 173559
6.8%
e 162965
6.3%
r 105298
 
4.1%
c 99674
 
3.9%
Other values (51) 289632
11.3%
Thai
ValueCountFrequency (%)
5
13.2%
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (10) 10
26.3%
Arabic
ValueCountFrequency (%)
ا 23
15.5%
ل 20
13.5%
ة 13
8.8%
ع 12
8.1%
ي 10
 
6.8%
س 10
 
6.8%
ن 10
 
6.8%
د 8
 
5.4%
م 8
 
5.4%
و 7
 
4.7%
Other values (8) 27
18.2%
Common
ValueCountFrequency (%)
- 180211
95.9%
, 6274
 
3.3%
1295
 
0.7%
: 194
 
0.1%
' 28
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
Cyrillic
ValueCountFrequency (%)
а 3
33.3%
з 1
 
11.1%
н 1
 
11.1%
т 1
 
11.1%
с 1
 
11.1%
х 1
 
11.1%
К 1
 
11.1%
Han
ValueCountFrequency (%)
7
38.9%
7
38.9%
2
 
11.1%
2
 
11.1%
Devanagari
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2580776
93.6%
None 176314
 
6.4%
Arabic 148
 
< 0.1%
Thai 38
 
< 0.1%
CJK 18
 
< 0.1%
IPA Ext 9
 
< 0.1%
Cyrillic 9
 
< 0.1%
Devanagari 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 393767
15.3%
t 352808
13.7%
a 305218
11.8%
n 297617
11.5%
i 209391
8.1%
- 180211
7.0%
U 179165
6.9%
e 162965
6.3%
r 105298
 
4.1%
c 99674
 
3.9%
Other values (48) 294662
11.4%
None
ValueCountFrequency (%)
É 173559
98.4%
é 1674
 
0.9%
è 638
 
0.4%
ï 286
 
0.2%
ç 63
 
< 0.1%
ë 43
 
< 0.1%
ô 28
 
< 0.1%
ê 17
 
< 0.1%
Î 6
 
< 0.1%
Arabic
ValueCountFrequency (%)
ا 23
15.5%
ل 20
13.5%
ة 13
8.8%
ع 12
8.1%
ي 10
 
6.8%
س 10
 
6.8%
ن 10
 
6.8%
د 8
 
5.4%
م 8
 
5.4%
و 7
 
4.7%
Other values (8) 27
18.2%
IPA Ext
ValueCountFrequency (%)
ə 9
100.0%
CJK
ValueCountFrequency (%)
7
38.9%
7
38.9%
2
 
11.1%
2
 
11.1%
Thai
ValueCountFrequency (%)
5
13.2%
4
 
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
2
 
5.3%
Other values (10) 10
26.3%
Cyrillic
ValueCountFrequency (%)
а 3
33.3%
з 1
 
11.1%
н 1
 
11.1%
т 1
 
11.1%
с 1
 
11.1%
х 1
 
11.1%
К 1
 
11.1%
Devanagari
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

product_name
Text

MISSING 

Distinct221347
Distinct (%)73.0%
Missing17762
Missing (%)5.5%
Memory size2.4 MiB
2024-06-07T17:44:36.507604image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length234
Median length163
Mean length25.935392
Min length1

Characters and Unicode

Total characters7858683
Distinct characters1170
Distinct categories21 ?
Distinct scripts12 ?
Distinct blocks21 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique196915 ?
Unique (%)65.0%

Sample

1st rowFarine de blé noir
2nd rowBanana Chips Sweetened (Whole)
3rd rowPeanuts
4th rowOrganic Salted Nut Mix
5th rowOrganic Polenta
ValueCountFrequency (%)
de 27212
 
2.3%
26484
 
2.2%
chocolate 11502
 
1.0%
sauce 10673
 
0.9%
cheese 10419
 
0.9%
organic 9469
 
0.8%
with 8416
 
0.7%
au 8117
 
0.7%
mix 7317
 
0.6%
à 6356
 
0.5%
Other values (56210) 1079210
89.5%
2024-06-07T17:44:36.933499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
909456
 
11.6%
e 784076
 
10.0%
a 596106
 
7.6%
r 459723
 
5.8%
i 451258
 
5.7%
o 406022
 
5.2%
t 365920
 
4.7%
n 354996
 
4.5%
s 337707
 
4.3%
l 323388
 
4.1%
Other values (1160) 2870031
36.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5721495
72.8%
Uppercase Letter 1009124
 
12.8%
Space Separator 909509
 
11.6%
Other Punctuation 142278
 
1.8%
Decimal Number 44167
 
0.6%
Dash Punctuation 16349
 
0.2%
Open Punctuation 5273
 
0.1%
Close Punctuation 5272
 
0.1%
Other Letter 2965
 
< 0.1%
Math Symbol 1241
 
< 0.1%
Other values (11) 1010
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
ا 53
 
1.8%
ي 43
 
1.5%
ل 40
 
1.3%
ו 40
 
1.3%
37
 
1.2%
34
 
1.1%
32
 
1.1%
و 31
 
1.0%
31
 
1.0%
30
 
1.0%
Other values (768) 2594
87.5%
Lowercase Letter
ValueCountFrequency (%)
e 784076
13.7%
a 596106
10.4%
r 459723
 
8.0%
i 451258
 
7.9%
o 406022
 
7.1%
t 365920
 
6.4%
n 354996
 
6.2%
s 337707
 
5.9%
l 323388
 
5.7%
u 248650
 
4.3%
Other values (154) 1393649
24.4%
Uppercase Letter
ValueCountFrequency (%)
C 154323
15.3%
S 121001
12.0%
P 86294
 
8.6%
B 81287
 
8.1%
M 64630
 
6.4%
F 49108
 
4.9%
T 42996
 
4.3%
G 41986
 
4.2%
A 38382
 
3.8%
O 38335
 
3.8%
Other values (102) 290782
28.8%
Other Punctuation
ValueCountFrequency (%)
, 86486
60.8%
& 21619
 
15.2%
' 16194
 
11.4%
% 7926
 
5.6%
. 3741
 
2.6%
; 2711
 
1.9%
! 1289
 
0.9%
/ 864
 
0.6%
: 741
 
0.5%
* 225
 
0.2%
Other values (17) 482
 
0.3%
Nonspacing Mark
ValueCountFrequency (%)
30
22.1%
20
14.7%
18
13.2%
16
11.8%
14
10.3%
́ 8
 
5.9%
7
 
5.1%
5
 
3.7%
4
 
2.9%
4
 
2.9%
Other values (6) 10
 
7.4%
Other Symbol
ValueCountFrequency (%)
® 131
44.4%
° 120
40.7%
9
 
3.1%
8
 
2.7%
8
 
2.7%
5
 
1.7%
💧 3
 
1.0%
3
 
1.0%
2
 
0.7%
© 2
 
0.7%
Other values (4) 4
 
1.4%
Decimal Number
ValueCountFrequency (%)
0 13211
29.9%
1 8496
19.2%
2 6505
14.7%
5 3672
 
8.3%
4 3171
 
7.2%
3 3132
 
7.1%
6 2079
 
4.7%
8 1780
 
4.0%
7 1338
 
3.0%
9 783
 
1.8%
Math Symbol
ValueCountFrequency (%)
+ 1212
97.7%
| 12
 
1.0%
= 6
 
0.5%
~ 3
 
0.2%
> 2
 
0.2%
< 2
 
0.2%
× 2
 
0.2%
1
 
0.1%
1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 5219
99.0%
[ 42
 
0.8%
{ 7
 
0.1%
3
 
0.1%
1
 
< 0.1%
1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 16331
99.9%
11
 
0.1%
6
 
< 0.1%
1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 5224
99.1%
] 41
 
0.8%
} 6
 
0.1%
1
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
34
79.1%
4
 
9.3%
4
 
9.3%
1
 
2.3%
Control
ValueCountFrequency (%)
 9
47.4%
œ 6
31.6%
 2
 
10.5%
Œ 2
 
10.5%
Space Separator
ValueCountFrequency (%)
909456
> 99.9%
  50
 
< 0.1%
  3
 
< 0.1%
Initial Punctuation
ValueCountFrequency (%)
« 177
93.7%
11
 
5.8%
1
 
0.5%
Final Punctuation
ValueCountFrequency (%)
» 175
88.8%
15
 
7.6%
7
 
3.6%
Currency Symbol
ValueCountFrequency (%)
$ 49
76.6%
14
 
21.9%
¢ 1
 
1.6%
Modifier Symbol
ValueCountFrequency (%)
` 24
66.7%
´ 11
30.6%
¨ 1
 
2.8%
Connector Punctuation
ValueCountFrequency (%)
_ 29
100.0%
Format
ValueCountFrequency (%)
1
100.0%
Other Number
ValueCountFrequency (%)
² 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6702167
85.3%
Common 1124962
 
14.3%
Cyrillic 28021
 
0.4%
Han 1065
 
< 0.1%
Thai 528
 
< 0.1%
Greek 443
 
< 0.1%
Arabic 431
 
< 0.1%
Hebrew 290
 
< 0.1%
Katakana 285
 
< 0.1%
Hiragana 246
 
< 0.1%
Other values (2) 245
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
37
 
3.5%
32
 
3.0%
26
 
2.4%
23
 
2.2%
21
 
2.0%
19
 
1.8%
18
 
1.7%
17
 
1.6%
16
 
1.5%
15
 
1.4%
Other values (427) 841
79.0%
Latin
ValueCountFrequency (%)
e 784076
 
11.7%
a 596106
 
8.9%
r 459723
 
6.9%
i 451258
 
6.7%
o 406022
 
6.1%
t 365920
 
5.5%
n 354996
 
5.3%
s 337707
 
5.0%
l 323388
 
4.8%
u 248650
 
3.7%
Other values (154) 2374321
35.4%
Hangul
ValueCountFrequency (%)
6
 
2.6%
6
 
2.6%
5
 
2.2%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (125) 185
80.4%
Common
ValueCountFrequency (%)
909456
80.8%
, 86486
 
7.7%
& 21619
 
1.9%
- 16331
 
1.5%
' 16194
 
1.4%
0 13211
 
1.2%
1 8496
 
0.8%
% 7926
 
0.7%
2 6505
 
0.6%
) 5224
 
0.5%
Other values (89) 33514
 
3.0%
Cyrillic
ValueCountFrequency (%)
о 2981
 
10.6%
а 2603
 
9.3%
н 1974
 
7.0%
е 1964
 
7.0%
и 1708
 
6.1%
р 1583
 
5.6%
с 1523
 
5.4%
к 1410
 
5.0%
л 1316
 
4.7%
т 999
 
3.6%
Other values (55) 9960
35.5%
Katakana
ValueCountFrequency (%)
23
 
8.1%
18
 
6.3%
17
 
6.0%
15
 
5.3%
13
 
4.6%
13
 
4.6%
10
 
3.5%
9
 
3.2%
9
 
3.2%
8
 
2.8%
Other values (52) 150
52.6%
Hiragana
ValueCountFrequency (%)
25
 
10.2%
15
 
6.1%
15
 
6.1%
14
 
5.7%
10
 
4.1%
10
 
4.1%
9
 
3.7%
8
 
3.3%
7
 
2.8%
7
 
2.8%
Other values (45) 126
51.2%
Greek
ValueCountFrequency (%)
α 43
 
9.7%
ο 36
 
8.1%
ι 29
 
6.5%
ρ 27
 
6.1%
τ 21
 
4.7%
λ 19
 
4.3%
κ 16
 
3.6%
ς 16
 
3.6%
υ 16
 
3.6%
ν 15
 
3.4%
Other values (38) 205
46.3%
Thai
ValueCountFrequency (%)
34
 
6.4%
31
 
5.9%
30
 
5.7%
30
 
5.7%
23
 
4.4%
23
 
4.4%
22
 
4.2%
21
 
4.0%
20
 
3.8%
18
 
3.4%
Other values (33) 276
52.3%
Arabic
ValueCountFrequency (%)
ا 53
 
12.3%
ي 43
 
10.0%
ل 40
 
9.3%
و 31
 
7.2%
م 26
 
6.0%
ن 25
 
5.8%
ر 20
 
4.6%
ب 20
 
4.6%
ة 16
 
3.7%
ك 15
 
3.5%
Other values (20) 142
32.9%
Hebrew
ValueCountFrequency (%)
ו 40
13.8%
י 27
 
9.3%
מ 24
 
8.3%
ל 23
 
7.9%
פ 20
 
6.9%
ר 19
 
6.6%
ק 19
 
6.6%
ת 14
 
4.8%
ח 12
 
4.1%
ט 10
 
3.4%
Other values (17) 82
28.3%
Inherited
ValueCountFrequency (%)
́ 8
53.3%
3
 
20.0%
̀ 2
 
13.3%
̈ 1
 
6.7%
1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7757321
98.7%
None 70083
 
0.9%
Cyrillic 28021
 
0.4%
CJK 1065
 
< 0.1%
Thai 528
 
< 0.1%
Arabic 431
 
< 0.1%
Katakana 311
 
< 0.1%
Hebrew 290
 
< 0.1%
Hiragana 246
 
< 0.1%
Hangul 230
 
< 0.1%
Other values (11) 157
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
909456
 
11.7%
e 784076
 
10.1%
a 596106
 
7.7%
r 459723
 
5.9%
i 451258
 
5.8%
o 406022
 
5.2%
t 365920
 
4.7%
n 354996
 
4.6%
s 337707
 
4.4%
l 323388
 
4.2%
Other values (84) 2768669
35.7%
None
ValueCountFrequency (%)
é 37970
54.2%
à 6325
 
9.0%
è 5876
 
8.4%
â 3186
 
4.5%
ê 1852
 
2.6%
ü 1672
 
2.4%
û 1506
 
2.1%
ä 1185
 
1.7%
ô 1080
 
1.5%
É 937
 
1.3%
Other values (179) 8494
 
12.1%
Cyrillic
ValueCountFrequency (%)
о 2981
 
10.6%
а 2603
 
9.3%
н 1974
 
7.0%
е 1964
 
7.0%
и 1708
 
6.1%
р 1583
 
5.6%
с 1523
 
5.4%
к 1410
 
5.0%
л 1316
 
4.7%
т 999
 
3.6%
Other values (55) 9960
35.5%
Arabic
ValueCountFrequency (%)
ا 53
 
12.3%
ي 43
 
10.0%
ل 40
 
9.3%
و 31
 
7.2%
م 26
 
6.0%
ن 25
 
5.8%
ر 20
 
4.6%
ب 20
 
4.6%
ة 16
 
3.7%
ك 15
 
3.5%
Other values (20) 142
32.9%
Hebrew
ValueCountFrequency (%)
ו 40
13.8%
י 27
 
9.3%
מ 24
 
8.3%
ל 23
 
7.9%
פ 20
 
6.9%
ר 19
 
6.6%
ק 19
 
6.6%
ת 14
 
4.8%
ח 12
 
4.1%
ט 10
 
3.4%
Other values (17) 82
28.3%
CJK
ValueCountFrequency (%)
37
 
3.5%
32
 
3.0%
26
 
2.4%
23
 
2.2%
21
 
2.0%
19
 
1.8%
18
 
1.7%
17
 
1.6%
16
 
1.5%
15
 
1.4%
Other values (427) 841
79.0%
Thai
ValueCountFrequency (%)
34
 
6.4%
31
 
5.9%
30
 
5.7%
30
 
5.7%
23
 
4.4%
23
 
4.4%
22
 
4.2%
21
 
4.0%
20
 
3.8%
18
 
3.4%
Other values (33) 276
52.3%
Katakana
ValueCountFrequency (%)
34
 
10.9%
23
 
7.4%
18
 
5.8%
17
 
5.5%
15
 
4.8%
13
 
4.2%
13
 
4.2%
10
 
3.2%
9
 
2.9%
9
 
2.9%
Other values (49) 150
48.2%
Hiragana
ValueCountFrequency (%)
25
 
10.2%
15
 
6.1%
15
 
6.1%
14
 
5.7%
10
 
4.1%
10
 
4.1%
9
 
3.7%
8
 
3.3%
7
 
2.8%
7
 
2.8%
Other values (45) 126
51.2%
Punctuation
ValueCountFrequency (%)
15
18.5%
13
16.0%
11
13.6%
11
13.6%
11
13.6%
7
8.6%
6
 
7.4%
3
 
3.7%
1
 
1.2%
1
 
1.2%
Other values (2) 2
 
2.5%
Currency Symbols
ValueCountFrequency (%)
14
100.0%
Letterlike Symbols
ValueCountFrequency (%)
9
52.9%
5
29.4%
3
 
17.6%
Misc Symbols
ValueCountFrequency (%)
8
50.0%
8
50.0%
Diacriticals
ValueCountFrequency (%)
́ 8
72.7%
̀ 2
 
18.2%
̈ 1
 
9.1%
Hangul
ValueCountFrequency (%)
6
 
2.6%
6
 
2.6%
5
 
2.2%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
4
 
1.7%
Other values (125) 185
80.4%
VS
ValueCountFrequency (%)
3
75.0%
1
 
25.0%
Specials
ValueCountFrequency (%)
2
100.0%
Latin Ext Additional
ValueCountFrequency (%)
1
11.1%
ế 1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Enclosed Alphanum Sup
ValueCountFrequency (%)
🅫 1
100.0%
Dingbats
ValueCountFrequency (%)
1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

brands
Categorical

HIGH CARDINALITY  MISSING 

Distinct58784
Distinct (%)20.1%
Missing28412
Missing (%)8.9%
Memory size3.7 MiB
Carrefour
 
2978
Auchan
 
2340
U
 
2050
Meijer
 
1995
Leader Price
 
1700
Other values (58779)
281297 

Length

Max length228
Median length155
Mean length15.327531
Min length1

Characters and Unicode

Total characters4481157
Distinct characters633
Distinct categories18 ?
Distinct scripts12 ?
Distinct blocks14 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique34837 ?
Unique (%)11.9%

Sample

1st rowFerme t'y R'nao
2nd rowTorn & Glasser
3rd rowGrizzlies
4th rowBob's Red Mill
5th rowUnfi

Common Values

ValueCountFrequency (%)
Carrefour 2978
 
0.9%
Auchan 2340
 
0.7%
U 2050
 
0.6%
Meijer 1995
 
0.6%
Leader Price 1700
 
0.5%
Kroger 1660
 
0.5%
Casino 1608
 
0.5%
Ahold 1370
 
0.4%
Spartan 1341
 
0.4%
Roundy's 1299
 
0.4%
Other values (58774) 274019
85.4%
(Missing) 28412
 
8.9%

Length

2024-06-07T17:44:37.093103image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
inc 35213
 
5.3%
foods 14341
 
2.1%
llc 8873
 
1.3%
company 8786
 
1.3%
8544
 
1.3%
co 7706
 
1.2%
food 7136
 
1.1%
the 5004
 
0.7%
carrefour 4519
 
0.7%
market 4124
 
0.6%
Other values (39713) 565196
84.4%

Most occurring characters

ValueCountFrequency (%)
442357
 
9.9%
e 380059
 
8.5%
a 327513
 
7.3%
r 296410
 
6.6%
o 286876
 
6.4%
n 247897
 
5.5%
i 230699
 
5.1%
s 199096
 
4.4%
t 171925
 
3.8%
l 166222
 
3.7%
Other values (623) 1732103
38.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3155292
70.4%
Uppercase Letter 689398
 
15.4%
Space Separator 442359
 
9.9%
Other Punctuation 172153
 
3.8%
Dash Punctuation 10725
 
0.2%
Decimal Number 7074
 
0.2%
Close Punctuation 1282
 
< 0.1%
Open Punctuation 1282
 
< 0.1%
Other Letter 982
 
< 0.1%
Math Symbol 298
 
< 0.1%
Other values (8) 312
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
º 46
 
4.7%
42
 
4.3%
ا 41
 
4.2%
37
 
3.8%
ل 23
 
2.3%
23
 
2.3%
و 22
 
2.2%
19
 
1.9%
ن 19
 
1.9%
18
 
1.8%
Other values (314) 692
70.5%
Lowercase Letter
ValueCountFrequency (%)
e 380059
12.0%
a 327513
10.4%
r 296410
9.4%
o 286876
9.1%
n 247897
 
7.9%
i 230699
 
7.3%
s 199096
 
6.3%
t 171925
 
5.4%
l 166222
 
5.3%
c 133205
 
4.2%
Other values (133) 715390
22.7%
Uppercase Letter
ValueCountFrequency (%)
C 74077
 
10.7%
S 59861
 
8.7%
F 49978
 
7.2%
M 49416
 
7.2%
I 47411
 
6.9%
B 44253
 
6.4%
L 42357
 
6.1%
P 35235
 
5.1%
A 31592
 
4.6%
T 29764
 
4.3%
Other values (93) 225454
32.7%
Other Punctuation
ValueCountFrequency (%)
. 57999
33.7%
, 57382
33.3%
' 29445
17.1%
/ 13588
 
7.9%
& 8904
 
5.2%
: 3333
 
1.9%
! 1183
 
0.7%
" 150
 
0.1%
% 34
 
< 0.1%
; 31
 
< 0.1%
Other values (9) 104
 
0.1%
Decimal Number
ValueCountFrequency (%)
3 1340
18.9%
5 1239
17.5%
6 1108
15.7%
1 675
9.5%
2 656
9.3%
0 626
8.8%
7 510
 
7.2%
4 385
 
5.4%
9 285
 
4.0%
8 250
 
3.5%
Nonspacing Mark
ValueCountFrequency (%)
23
34.3%
12
17.9%
12
17.9%
8
 
11.9%
6
 
9.0%
4
 
6.0%
́ 1
 
1.5%
1
 
1.5%
Space Separator
ValueCountFrequency (%)
442357
> 99.9%
  1
 
< 0.1%
  1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 1268
98.9%
] 12
 
0.9%
2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 1267
98.8%
[ 11
 
0.9%
4
 
0.3%
Math Symbol
ValueCountFrequency (%)
+ 295
99.0%
| 2
 
0.7%
~ 1
 
0.3%
Other Symbol
ValueCountFrequency (%)
® 20
64.5%
° 6
 
19.4%
5
 
16.1%
Dash Punctuation
ValueCountFrequency (%)
- 10718
99.9%
7
 
0.1%
Currency Symbol
ValueCountFrequency (%)
$ 71
86.6%
11
 
13.4%
Final Punctuation
ValueCountFrequency (%)
» 45
83.3%
9
 
16.7%
Modifier Symbol
ValueCountFrequency (%)
´ 12
54.5%
` 10
45.5%
Initial Punctuation
ValueCountFrequency (%)
« 47
100.0%
Other Number
ValueCountFrequency (%)
³ 6
100.0%
Modifier Letter
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3833890
85.6%
Common 635418
 
14.2%
Cyrillic 10645
 
0.2%
Thai 330
 
< 0.1%
Han 245
 
< 0.1%
Arabic 219
 
< 0.1%
Greek 201
 
< 0.1%
Hebrew 73
 
< 0.1%
Katakana 59
 
< 0.1%
Hangul 53
 
< 0.1%
Other values (2) 24
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
6
 
2.4%
5
 
2.0%
5
 
2.0%
5
 
2.0%
4
 
1.6%
4
 
1.6%
3
 
1.2%
3
 
1.2%
3
 
1.2%
3
 
1.2%
Other values (146) 204
83.3%
Latin
ValueCountFrequency (%)
e 380059
 
9.9%
a 327513
 
8.5%
r 296410
 
7.7%
o 286876
 
7.5%
n 247897
 
6.5%
i 230699
 
6.0%
s 199096
 
5.2%
t 171925
 
4.5%
l 166222
 
4.3%
c 133205
 
3.5%
Other values (124) 1393988
36.4%
Cyrillic
ValueCountFrequency (%)
о 946
 
8.9%
а 919
 
8.6%
е 794
 
7.5%
р 675
 
6.3%
н 650
 
6.1%
и 616
 
5.8%
к 566
 
5.3%
с 506
 
4.8%
т 373
 
3.5%
л 366
 
3.4%
Other values (53) 4234
39.8%
Common
ValueCountFrequency (%)
442357
69.6%
. 57999
 
9.1%
, 57382
 
9.0%
' 29445
 
4.6%
/ 13588
 
2.1%
- 10718
 
1.7%
& 8904
 
1.4%
: 3333
 
0.5%
3 1340
 
0.2%
) 1268
 
0.2%
Other values (45) 9084
 
1.4%
Greek
ValueCountFrequency (%)
ν 17
 
8.5%
α 16
 
8.0%
ο 11
 
5.5%
Α 10
 
5.0%
ς 8
 
4.0%
τ 8
 
4.0%
Κ 8
 
4.0%
κ 7
 
3.5%
ρ 7
 
3.5%
ι 7
 
3.5%
Other values (40) 102
50.7%
Hangul
ValueCountFrequency (%)
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
Other values (37) 37
69.8%
Thai
ValueCountFrequency (%)
42
 
12.7%
37
 
11.2%
23
 
7.0%
23
 
7.0%
19
 
5.8%
18
 
5.5%
12
 
3.6%
12
 
3.6%
12
 
3.6%
12
 
3.6%
Other values (25) 120
36.4%
Katakana
ValueCountFrequency (%)
5
 
8.5%
4
 
6.8%
4
 
6.8%
4
 
6.8%
4
 
6.8%
3
 
5.1%
3
 
5.1%
3
 
5.1%
2
 
3.4%
2
 
3.4%
Other values (20) 25
42.4%
Arabic
ValueCountFrequency (%)
ا 41
18.7%
ل 23
10.5%
و 22
10.0%
ن 19
8.7%
ر 18
8.2%
ي 17
7.8%
ك 16
 
7.3%
س 8
 
3.7%
ف 8
 
3.7%
ب 7
 
3.2%
Other values (16) 40
18.3%
Hebrew
ValueCountFrequency (%)
ו 9
12.3%
י 7
 
9.6%
ב 6
 
8.2%
ר 6
 
8.2%
ק 5
 
6.8%
מ 5
 
6.8%
ת 5
 
6.8%
פ 4
 
5.5%
ה 4
 
5.5%
נ 4
 
5.5%
Other values (11) 18
24.7%
Hiragana
ValueCountFrequency (%)
3
13.0%
3
13.0%
3
13.0%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (5) 5
21.7%
Inherited
ValueCountFrequency (%)
́ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4449117
99.3%
None 20347
 
0.5%
Cyrillic 10645
 
0.2%
Thai 330
 
< 0.1%
CJK 245
 
< 0.1%
Arabic 220
 
< 0.1%
Hebrew 73
 
< 0.1%
Katakana 62
 
< 0.1%
Hangul 53
 
< 0.1%
Punctuation 25
 
< 0.1%
Other values (4) 40
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
442357
 
9.9%
e 380059
 
8.5%
a 327513
 
7.4%
r 296410
 
6.7%
o 286876
 
6.4%
n 247897
 
5.6%
i 230699
 
5.2%
s 199096
 
4.5%
t 171925
 
3.9%
l 166222
 
3.7%
Other values (77) 1700063
38.2%
None
ValueCountFrequency (%)
é 11056
54.3%
è 3546
 
17.4%
ü 729
 
3.6%
ó 573
 
2.8%
í 455
 
2.2%
ô 429
 
2.1%
â 388
 
1.9%
ä 325
 
1.6%
ê 296
 
1.5%
î 283
 
1.4%
Other values (134) 2267
 
11.1%
Cyrillic
ValueCountFrequency (%)
о 946
 
8.9%
а 919
 
8.6%
е 794
 
7.5%
р 675
 
6.3%
н 650
 
6.1%
и 616
 
5.8%
к 566
 
5.3%
с 506
 
4.8%
т 373
 
3.5%
л 366
 
3.4%
Other values (53) 4234
39.8%
Thai
ValueCountFrequency (%)
42
 
12.7%
37
 
11.2%
23
 
7.0%
23
 
7.0%
19
 
5.8%
18
 
5.5%
12
 
3.6%
12
 
3.6%
12
 
3.6%
12
 
3.6%
Other values (25) 120
36.4%
Arabic
ValueCountFrequency (%)
ا 41
18.6%
ل 23
10.5%
و 22
10.0%
ن 19
8.6%
ر 18
8.2%
ي 17
7.7%
ك 16
 
7.3%
س 8
 
3.6%
ف 8
 
3.6%
ب 7
 
3.2%
Other values (17) 41
18.6%
Currency Symbols
ValueCountFrequency (%)
11
100.0%
Hebrew
ValueCountFrequency (%)
ו 9
12.3%
י 7
 
9.6%
ב 6
 
8.2%
ר 6
 
8.2%
ק 5
 
6.8%
מ 5
 
6.8%
ת 5
 
6.8%
פ 4
 
5.5%
ה 4
 
5.5%
נ 4
 
5.5%
Other values (11) 18
24.7%
Punctuation
ValueCountFrequency (%)
9
36.0%
7
28.0%
7
28.0%
2
 
8.0%
CJK
ValueCountFrequency (%)
6
 
2.4%
5
 
2.0%
5
 
2.0%
5
 
2.0%
4
 
1.6%
4
 
1.6%
3
 
1.2%
3
 
1.2%
3
 
1.2%
3
 
1.2%
Other values (146) 204
83.3%
Letterlike Symbols
ValueCountFrequency (%)
5
100.0%
Katakana
ValueCountFrequency (%)
5
 
8.1%
4
 
6.5%
4
 
6.5%
4
 
6.5%
4
 
6.5%
3
 
4.8%
3
 
4.8%
3
 
4.8%
3
 
4.8%
2
 
3.2%
Other values (21) 27
43.5%
Hiragana
ValueCountFrequency (%)
3
13.0%
3
13.0%
3
13.0%
2
 
8.7%
2
 
8.7%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
1
 
4.3%
Other values (5) 5
21.7%
Hangul
ValueCountFrequency (%)
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
1
 
1.9%
1
 
1.9%
1
 
1.9%
1
 
1.9%
Other values (37) 37
69.8%
Diacriticals
ValueCountFrequency (%)
́ 1
100.0%

energy_100g
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct3997
Distinct (%)1.5%
Missing59659
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean1141.9146
Minimum0
Maximum3251373
Zeros8909
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:37.229733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile71
Q1377
median1100
Q31674
95-th percentile2389
Maximum3251373
Range3251373
Interquartile range (IQR)1297

Descriptive statistics

Standard deviation6447.1541
Coefficient of variation (CV)5.6459161
Kurtosis247388.17
Mean1141.9146
Median Absolute Deviation (MAD)657
Skewness491.00398
Sum2.9816875 × 108
Variance41565796
MonotonicityNot monotonic
2024-06-07T17:44:37.573815image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8909
 
2.8%
2092 5075
 
1.6%
1674 4012
 
1.3%
1494 3916
 
1.2%
1644 3282
 
1.0%
1393 3225
 
1.0%
1046 2945
 
0.9%
1569 2825
 
0.9%
1795 2350
 
0.7%
1197 2314
 
0.7%
Other values (3987) 222260
69.3%
(Missing) 59659
 
18.6%
ValueCountFrequency (%)
0 8909
2.8%
0.02 1
 
< 0.1%
0.42 1
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.8 7
 
< 0.1%
0.9 4
 
< 0.1%
0.92 4
 
< 0.1%
1 50
 
< 0.1%
1.1 1
 
< 0.1%
ValueCountFrequency (%)
3251373 1
< 0.1%
231199 1
< 0.1%
182764 1
< 0.1%
110579 1
< 0.1%
94140 1
< 0.1%
87217 1
< 0.1%
69292 1
< 0.1%
26861 1
< 0.1%
22000 1
< 0.1%
18700 1
< 0.1%

salt_100g
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct5586
Distinct (%)2.2%
Missing65262
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean2.0286239
Minimum0
Maximum64312.8
Zeros34174
Zeros (%)10.7%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:37.709455image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0635
median0.58166
Q31.37414
95-th percentile4.064
Maximum64312.8
Range64312.8
Interquartile range (IQR)1.31064

Descriptive statistics

Standard deviation128.26945
Coefficient of variation (CV)63.229784
Kurtosis247314.47
Mean2.0286239
Median Absolute Deviation (MAD)0.55666
Skewness493.50379
Sum518333.71
Variance16453.053
MonotonicityNot monotonic
2024-06-07T17:44:37.844090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34174
 
10.7%
0.01 3692
 
1.2%
0.1 3467
 
1.1%
1 2231
 
0.7%
0.0254 2093
 
0.7%
1.27 1941
 
0.6%
1.63322 1825
 
0.6%
0.127 1779
 
0.6%
0.03 1636
 
0.5%
1.3 1551
 
0.5%
Other values (5576) 201121
62.7%
(Missing) 65262
 
20.3%
ValueCountFrequency (%)
0 34174
10.7%
5 × 10-81
 
< 0.1%
9.999999 × 10-82
 
< 0.1%
1 × 10-61
 
< 0.1%
5 × 10-61
 
< 0.1%
7.874 × 10-61
 
< 0.1%
1 × 10-55
 
< 0.1%
1.3 × 10-54
 
< 0.1%
2 × 10-51
 
< 0.1%
2.413 × 10-51
 
< 0.1%
ValueCountFrequency (%)
64312.8 1
< 0.1%
3556 1
< 0.1%
3048 1
< 0.1%
2452.41318 1
< 0.1%
2177.14322 1
< 0.1%
2032 1
< 0.1%
1799.16582 1
< 0.1%
1669.14322 1
< 0.1%
1318.38192 1
< 0.1%
1139.1519 1
< 0.1%

sodium_100g
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct5291
Distinct (%)2.1%
Missing65309
Missing (%)20.4%
Infinite0
Infinite (%)0.0%
Mean0.79881546
Minimum0
Maximum25320
Zeros34131
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:37.979728image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.025
median0.229
Q30.541
95-th percentile1.6
Maximum25320
Range25320
Interquartile range (IQR)0.516

Descriptive statistics

Standard deviation50.504428
Coefficient of variation (CV)63.22415
Kurtosis247269.02
Mean0.79881546
Median Absolute Deviation (MAD)0.21915748
Skewness493.45847
Sum204067.79
Variance2550.6972
MonotonicityNot monotonic
2024-06-07T17:44:38.117360image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34131
 
10.6%
0.003937007874 3687
 
1.1%
0.03937007874 3451
 
1.1%
0.3937007874 2216
 
0.7%
0.01 2092
 
0.7%
0.5 1939
 
0.6%
0.01181102362 1927
 
0.6%
0.643 1848
 
0.6%
0.05 1779
 
0.6%
0.5118110236 1545
 
0.5%
Other values (5281) 200848
62.6%
(Missing) 65309
 
20.4%
ValueCountFrequency (%)
0 34131
10.6%
1.968503937 × 10-81
 
< 0.1%
3.93700748 × 10-82
 
< 0.1%
3.937007874 × 10-71
 
< 0.1%
1.968503937 × 10-61
 
< 0.1%
3.1 × 10-61
 
< 0.1%
3.937007874 × 10-65
 
< 0.1%
5.118110236 × 10-64
 
< 0.1%
7.874015748 × 10-61
 
< 0.1%
9.5 × 10-61
 
< 0.1%
ValueCountFrequency (%)
25320 1
< 0.1%
1400 1
< 0.1%
1200 1
< 0.1%
965.517 1
< 0.1%
857.143 1
< 0.1%
800 1
< 0.1%
708.333 1
< 0.1%
657.143 1
< 0.1%
519.048 1
< 0.1%
448.485 1
< 0.1%

fiber_100g
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct1016
Distinct (%)0.5%
Missing119886
Missing (%)37.4%
Infinite0
Infinite (%)0.0%
Mean2.8621109
Minimum-6.7
Maximum5380
Zeros68833
Zeros (%)21.5%
Negative1
Negative (%)< 0.1%
Memory size2.4 MiB
2024-06-07T17:44:38.249007image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-6.7
5-th percentile0
Q10
median1.5
Q33.6
95-th percentile10.5
Maximum5380
Range5386.7
Interquartile range (IQR)3.6

Descriptive statistics

Standard deviation12.867578
Coefficient of variation (CV)4.4958348
Kurtosis151802.73
Mean2.8621109
Median Absolute Deviation (MAD)1.5
Skewness363.54781
Sum574958.02
Variance165.57456
MonotonicityNot monotonic
2024-06-07T17:44:38.383647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68833
21.5%
3.6 8525
 
2.7%
3.3 3991
 
1.2%
1.8 3886
 
1.2%
0.8 3829
 
1.2%
7.1 3707
 
1.2%
2 3531
 
1.1%
1.6 3428
 
1.1%
0.5 3419
 
1.1%
1.2 3278
 
1.0%
Other values (1006) 94459
29.4%
(Missing) 119886
37.4%
ValueCountFrequency (%)
-6.7 1
 
< 0.1%
0 68833
21.5%
0.0001 2
 
< 0.1%
0.0002 1
 
< 0.1%
0.001 16
 
< 0.1%
0.002 3
 
< 0.1%
0.004 1
 
< 0.1%
0.00416 1
 
< 0.1%
0.005 2
 
< 0.1%
0.01 72
 
< 0.1%
ValueCountFrequency (%)
5380 1
 
< 0.1%
250 1
 
< 0.1%
178 1
 
< 0.1%
166.7 1
 
< 0.1%
100 10
< 0.1%
99 1
 
< 0.1%
94.8 1
 
< 0.1%
92.4 1
 
< 0.1%
90 1
 
< 0.1%
88 2
 
< 0.1%

additives_n
Real number (ℝ)

MISSING  ZEROS 

Distinct31
Distinct (%)< 0.1%
Missing71833
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean1.9360245
Minimum0
Maximum31
Zeros94259
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:38.501332image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile7
Maximum31
Range31
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5020195
Coefficient of variation (CV)1.2923491
Kurtosis7.4179254
Mean1.9360245
Median Absolute Deviation (MAD)1
Skewness2.1753736
Sum481952
Variance6.2601014
MonotonicityNot monotonic
2024-06-07T17:44:38.616026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 94259
29.4%
1 46509
14.5%
2 36520
 
11.4%
3 23680
 
7.4%
4 15243
 
4.8%
5 10935
 
3.4%
6 7290
 
2.3%
7 4702
 
1.5%
8 3359
 
1.0%
9 2194
 
0.7%
Other values (21) 4248
 
1.3%
(Missing) 71833
22.4%
ValueCountFrequency (%)
0 94259
29.4%
1 46509
14.5%
2 36520
 
11.4%
3 23680
 
7.4%
4 15243
 
4.8%
5 10935
 
3.4%
6 7290
 
2.3%
7 4702
 
1.5%
8 3359
 
1.0%
9 2194
 
0.7%
ValueCountFrequency (%)
31 4
 
< 0.1%
29 2
 
< 0.1%
28 2
 
< 0.1%
27 2
 
< 0.1%
26 3
 
< 0.1%
25 11
< 0.1%
24 10
 
< 0.1%
23 15
< 0.1%
22 27
< 0.1%
21 21
< 0.1%

sugars_100g
Real number (ℝ)

MISSING  ZEROS 

Distinct4068
Distinct (%)1.7%
Missing75801
Missing (%)23.6%
Infinite0
Infinite (%)0.0%
Mean16.003484
Minimum-17.86
Maximum3520
Zeros37077
Zeros (%)11.6%
Negative7
Negative (%)< 0.1%
Memory size2.4 MiB
2024-06-07T17:44:38.742695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-17.86
5-th percentile0
Q11.3
median5.71
Q324
95-th percentile62.5
Maximum3520
Range3537.86
Interquartile range (IQR)22.7

Descriptive statistics

Standard deviation22.327284
Coefficient of variation (CV)1.3951515
Kurtosis2477.5694
Mean16.003484
Median Absolute Deviation (MAD)5.71
Skewness17.201619
Sum3920389.4
Variance498.50763
MonotonicityNot monotonic
2024-06-07T17:44:38.879326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37077
 
11.6%
3.57 7148
 
2.2%
0.5 4589
 
1.4%
3.33 3706
 
1.2%
1 2666
 
0.8%
20 2347
 
0.7%
6.67 2269
 
0.7%
10 2192
 
0.7%
50 2129
 
0.7%
2 2042
 
0.6%
Other values (4058) 178806
55.7%
(Missing) 75801
23.6%
ValueCountFrequency (%)
-17.86 1
 
< 0.1%
-6.67 1
 
< 0.1%
-6.25 1
 
< 0.1%
-3.57 1
 
< 0.1%
-1.2 1
 
< 0.1%
-0.8 1
 
< 0.1%
-0.1 1
 
< 0.1%
0 37077
11.6%
0.0001 8
 
< 0.1%
0.0005 1
 
< 0.1%
ValueCountFrequency (%)
3520 1
 
< 0.1%
166.67 1
 
< 0.1%
134 1
 
< 0.1%
110.71 1
 
< 0.1%
105 1
 
< 0.1%
104 1
 
< 0.1%
103.5 4
 
< 0.1%
103 1
 
< 0.1%
100.8 1
 
< 0.1%
100 1011
0.3%

fat_100g
Real number (ℝ)

MISSING  ZEROS 

Distinct3378
Distinct (%)1.4%
Missing76881
Missing (%)24.0%
Infinite0
Infinite (%)0.0%
Mean12.730379
Minimum0
Maximum714.29
Zeros64504
Zeros (%)20.1%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:39.012964image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q320
95-th percentile46.43
Maximum714.29
Range714.29
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.578747
Coefficient of variation (CV)1.3808503
Kurtosis17.184558
Mean12.730379
Median Absolute Deviation (MAD)5
Skewness2.4647045
Sum3104824.8
Variance309.01234
MonotonicityNot monotonic
2024-06-07T17:44:39.149598image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 64504
20.1%
25 3409
 
1.1%
0.5 3202
 
1.0%
32.14 2981
 
0.9%
20 2688
 
0.8%
1.79 2528
 
0.8%
28.57 2460
 
0.8%
0.1 2437
 
0.8%
21.43 2411
 
0.8%
10 2284
 
0.7%
Other values (3368) 154987
48.3%
(Missing) 76881
24.0%
ValueCountFrequency (%)
0 64504
20.1%
0.0001 2
 
< 0.1%
0.000133 1
 
< 0.1%
0.001 1
 
< 0.1%
0.003 1
 
< 0.1%
0.004 2
 
< 0.1%
0.005 3
 
< 0.1%
0.007 1
 
< 0.1%
0.01 43
 
< 0.1%
0.012 2
 
< 0.1%
ValueCountFrequency (%)
714.29 1
 
< 0.1%
380 1
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
100 1288
0.4%
99.9 16
 
< 0.1%
99.85 1
 
< 0.1%
99.82 1
 
< 0.1%
99.8 17
 
< 0.1%
99.7 5
 
< 0.1%

saturated_fat_100g
Real number (ℝ)

MISSING  ZEROS 

Distinct2197
Distinct (%)1.0%
Missing91218
Missing (%)28.4%
Infinite0
Infinite (%)0.0%
Mean5.1299323
Minimum0
Maximum550
Zeros68736
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:39.283241image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.79
Q37.14
95-th percentile20
Maximum550
Range550
Interquartile range (IQR)7.14

Descriptive statistics

Standard deviation8.0142381
Coefficient of variation (CV)1.5622503
Kurtosis116.64216
Mean5.1299323
Median Absolute Deviation (MAD)1.79
Skewness4.8175969
Sum1177596.5
Variance64.228013
MonotonicityNot monotonic
2024-06-07T17:44:39.407907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68736
21.4%
0.1 5355
 
1.7%
3.57 3487
 
1.1%
0.5 3302
 
1.0%
7.14 2880
 
0.9%
0.2 2601
 
0.8%
1 2444
 
0.8%
0.3 2335
 
0.7%
3.33 2213
 
0.7%
1.79 2190
 
0.7%
Other values (2187) 134011
41.8%
(Missing) 91218
28.4%
ValueCountFrequency (%)
0 68736
21.4%
0.0001 11
 
< 0.1%
0.001 30
 
< 0.1%
0.002 10
 
< 0.1%
0.003 4
 
< 0.1%
0.0032 1
 
< 0.1%
0.004 3
 
< 0.1%
0.005 11
 
< 0.1%
0.006 2
 
< 0.1%
0.00667 1
 
< 0.1%
ValueCountFrequency (%)
550 1
 
< 0.1%
210 1
 
< 0.1%
175.38 1
 
< 0.1%
100 12
< 0.1%
99.9 1
 
< 0.1%
99 2
 
< 0.1%
98 1
 
< 0.1%
96 2
 
< 0.1%
95.5 1
 
< 0.1%
95 5
< 0.1%

nutrition_score_uk_100g
Real number (ℝ)

MISSING  ZEROS 

Distinct55
Distinct (%)< 0.1%
Missing99562
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean9.0580489
Minimum-15
Maximum40
Zeros13588
Zeros (%)4.2%
Negative37361
Negative (%)11.6%
Memory size2.4 MiB
2024-06-07T17:44:39.531574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-5
Q11
median9
Q316
95-th percentile24
Maximum40
Range55
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.1835893
Coefficient of variation (CV)1.0138595
Kurtosis-1.0755201
Mean9.0580489
Median Absolute Deviation (MAD)8
Skewness0.1320062
Sum2003731
Variance84.338312
MonotonicityNot monotonic
2024-06-07T17:44:39.661230image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13588
 
4.2%
1 11932
 
3.7%
2 11083
 
3.5%
14 10689
 
3.3%
-1 8827
 
2.8%
13 8409
 
2.6%
12 8239
 
2.6%
11 8093
 
2.5%
3 7620
 
2.4%
20 7390
 
2.3%
Other values (45) 125340
39.1%
(Missing) 99562
31.0%
ValueCountFrequency (%)
-15 1
 
< 0.1%
-14 5
 
< 0.1%
-13 23
 
< 0.1%
-12 46
 
< 0.1%
-11 90
 
< 0.1%
-10 157
 
< 0.1%
-9 315
 
0.1%
-8 602
 
0.2%
-7 963
 
0.3%
-6 4926
1.5%
ValueCountFrequency (%)
40 3
 
< 0.1%
38 1
 
< 0.1%
37 2
 
< 0.1%
36 17
 
< 0.1%
35 34
 
< 0.1%
34 20
 
< 0.1%
33 101
< 0.1%
32 64
 
< 0.1%
31 76
 
< 0.1%
30 192
0.1%

nutrition_score_fr_100g
Real number (ℝ)

MISSING  ZEROS 

Distinct55
Distinct (%)< 0.1%
Missing99562
Missing (%)31.0%
Infinite0
Infinite (%)0.0%
Mean9.165535
Minimum-15
Maximum40
Zeros12763
Zeros (%)4.0%
Negative35706
Negative (%)11.1%
Memory size2.4 MiB
2024-06-07T17:44:39.790897image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-15
5-th percentile-5
Q11
median10
Q316
95-th percentile24
Maximum40
Range55
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0559029
Coefficient of variation (CV)0.98803866
Kurtosis-1.0188856
Mean9.165535
Median Absolute Deviation (MAD)8
Skewness0.11483636
Sum2027508
Variance82.009378
MonotonicityNot monotonic
2024-06-07T17:44:39.919540image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12763
 
4.0%
1 11268
 
3.5%
14 11253
 
3.5%
2 10604
 
3.3%
13 8827
 
2.8%
-1 8804
 
2.7%
12 8658
 
2.7%
11 8653
 
2.7%
3 7857
 
2.4%
15 7529
 
2.3%
Other values (45) 124994
39.0%
(Missing) 99562
31.0%
ValueCountFrequency (%)
-15 1
 
< 0.1%
-14 5
 
< 0.1%
-13 23
 
< 0.1%
-12 46
 
< 0.1%
-11 90
 
< 0.1%
-10 159
 
< 0.1%
-9 315
 
0.1%
-8 601
 
0.2%
-7 950
 
0.3%
-6 4925
1.5%
ValueCountFrequency (%)
40 4
 
< 0.1%
38 1
 
< 0.1%
37 3
 
< 0.1%
36 17
 
< 0.1%
35 36
 
< 0.1%
34 20
 
< 0.1%
33 105
< 0.1%
32 73
 
< 0.1%
31 79
 
< 0.1%
30 207
0.1%

nutrition_grade_fr
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing99562
Missing (%)31.0%
Memory size313.6 KiB
d
62763 
c
45538 
e
43030 
a
35634 
b
34245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters221210
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowd
2nd rowb
3rd rowd
4th rowc
5th rowd

Common Values

ValueCountFrequency (%)
d 62763
19.6%
c 45538
14.2%
e 43030
13.4%
a 35634
 
11.1%
b 34245
 
10.7%
(Missing) 99562
31.0%

Length

2024-06-07T17:44:40.038222image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-07T17:44:40.150922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
d 62763
28.4%
c 45538
20.6%
e 43030
19.5%
a 35634
16.1%
b 34245
15.5%

Most occurring characters

ValueCountFrequency (%)
d 62763
28.4%
c 45538
20.6%
e 43030
19.5%
a 35634
16.1%
b 34245
15.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 221210
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 62763
28.4%
c 45538
20.6%
e 43030
19.5%
a 35634
16.1%
b 34245
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 221210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 62763
28.4%
c 45538
20.6%
e 43030
19.5%
a 35634
16.1%
b 34245
15.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 221210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 62763
28.4%
c 45538
20.6%
e 43030
19.5%
a 35634
16.1%
b 34245
15.5%

cholesterol_100g
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct537
Distinct (%)0.4%
Missing176682
Missing (%)55.1%
Infinite0
Infinite (%)0.0%
Mean0.020071383
Minimum0
Maximum95.238
Zeros89441
Zeros (%)27.9%
Negative0
Negative (%)0.0%
Memory size2.4 MiB
2024-06-07T17:44:40.281569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.09
Maximum95.238
Range95.238
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.35806161
Coefficient of variation (CV)17.839408
Kurtosis51631.976
Mean0.020071383
Median Absolute Deviation (MAD)0
Skewness221.11781
Sum2892.0856
Variance0.12820811
MonotonicityNot monotonic
2024-06-07T17:44:40.414218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 89441
27.9%
0.071 2462
 
0.8%
0.107 2237
 
0.7%
0.012 1909
 
0.6%
0.089 1664
 
0.5%
0.054 1651
 
0.5%
0.018 1591
 
0.5%
0.004 1503
 
0.5%
0.036 1386
 
0.4%
0.008 1209
 
0.4%
Other values (527) 39037
 
12.2%
(Missing) 176682
55.1%
ValueCountFrequency (%)
0 89441
27.9%
4.5 × 10-51
 
< 0.1%
7.1 × 10-51
 
< 0.1%
0.0001 5
 
< 0.1%
0.0002 5
 
< 0.1%
0.0004 1
 
< 0.1%
0.000416 1
 
< 0.1%
0.00046 1
 
< 0.1%
0.0005 2
 
< 0.1%
0.0008 1
 
< 0.1%
ValueCountFrequency (%)
95.238 1
< 0.1%
70.588 1
< 0.1%
62.5 1
< 0.1%
13.846 1
< 0.1%
10.9 1
< 0.1%
1.58 1
< 0.1%
1.291 1
< 0.1%
1.25 1
< 0.1%
1.081 1
< 0.1%
0.996 1
< 0.1%

Interactions

2024-06-07T17:44:32.711170image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:18.675693image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.299352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.774405image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.147739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.483160image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.843523image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.201919image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:28.566242image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.158982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.421601image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.815907image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:18.844262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.444989image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.952932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.322308image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.652735image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.966233image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.328555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:28.900352image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.277697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.542281image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.931567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:18.988885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.569668image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.075628image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.434998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.771422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.088893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.470171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.025039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.394378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.659963image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.042266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.118510image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.703319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.188298image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.543701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.884115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.214532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.595835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.145689image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.507051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.770667image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.144000image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.259133image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.852874image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.305017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.650417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.012779image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.331246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.715517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.258389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.624733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.886359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.254702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.404749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.990505image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.422673image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.804975image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.136413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.449941image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.840187image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.382070image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.742452image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.006037image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.375413image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.551389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.152108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.546340image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.921692image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.259112image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.571602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.973823image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.520685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.860131image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.129712image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.488074image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.724936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.286710image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.674002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.037398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.378764image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.701229image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:28.097492image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.696219image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.980812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.257365image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.596812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.859562image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.416368image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.786727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.154068image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.498444image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.830882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:28.216178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.808965image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.090515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.375050image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.698542image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:19.977258image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.537047image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:22.899432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.262792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.614141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:26.971509image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:28.333912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:29.931587image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.201218image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.491740image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:33.798274image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:20.108894image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:21.651775image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:23.013134image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:24.372484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:25.727831image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:27.087225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:28.443567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:30.039330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:31.302951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-06-07T17:44:32.595464image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Missing values

2024-06-07T17:44:33.944879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-07T17:44:34.346819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

codecountries_frproduct_namebrandsenergy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gnutrition_grade_frcholesterol_100g
00000000003087FranceFarine de blé noirFerme t'y R'naoNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
10000000004530États-UnisBanana Chips Sweetened (Whole)NaN2243.00.000000.0003.60.014.2928.5728.5714.014.0d0.018
20000000004559États-UnisPeanutsTorn & Glasser1941.00.635000.2507.10.017.8617.860.000.00.0b0.000
30000000016087États-UnisOrganic Salted Nut MixGrizzlies2540.01.224280.4827.10.03.5757.145.3612.012.0dNaN
40000000016094États-UnisOrganic PolentaBob's Red Mill1552.0NaNNaN5.70.0NaN1.43NaNNaNNaNNaNNaN
50000000016100États-UnisBreadshop Honey Gone Nuts GranolaUnfi1933.0NaNNaN7.70.011.5418.271.92NaNNaNNaNNaN
60000000016117États-UnisOrganic Long Grain White RiceLundberg1490.0NaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaN
70000000016124États-UnisOrganic MuesliDaddy's Muesli1833.00.139700.0559.42.015.6218.754.697.07.0cNaN
80000000016193États-UnisOrganic Dark Chocolate MinisEqual Exchange2406.0NaNNaN7.50.042.5037.5022.50NaNNaNNaNNaN
90000000016513États-UnisOrganic Sunflower OilNapa Valley Naturals3586.0NaNNaNNaN0.0NaN100.007.14NaNNaNNaNNaN
codecountries_frproduct_namebrandsenergy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gnutrition_grade_frcholesterol_100g
3207629908278636246PologneSzprot w oleju roslinnymEvraFishNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32076399111250FranceThé vert Earl greyLobodis21.00.02540.010.20.00.50.20.20.02.0cNaN
3207649918FranceCheese cake thé vert, yuzuNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3207659935010000003FranceRillette d'oieSans marque,D.LambertNaNNaNNaNNaN0.0NaNNaNNaNNaNNaNNaNNaN
32076699410148Royaume-UniNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3207679948282780603RoumanieTomato & ricottaPanzaniNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
32076899567453États-UnisMint Melange Tea A Blend Of Peppermint, Lemon Grass And SpearmintTrader Joe's0.00.00000.000.00.00.00.00.00.00.0b0.0
3207699970229501521Chine乐吧泡菜味薯片乐吧NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3207709980282863788FranceTomates aux VermicellesKnorrNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
320771999990026839États-UnisSugar Free Drink Mix, Peach TeaMarket Pantry2092.00.00000.00NaN7.00.00.0NaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

codecountries_frproduct_namebrandsenergy_100gsalt_100gsodium_100gfiber_100gadditives_nsugars_100gfat_100gsaturated_fat_100gnutrition_score_uk_100gnutrition_score_fr_100gnutrition_grade_frcholesterol_100g# duplicates
1NaNen:fruit-yogurtsFranceNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN7
2NaNen:stirred-yogurtsFranceNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4
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